KeenKT: Knowledge Mastery-State Disambiguation for Knowledge Tracing
Zhifei Li, Lifan Chen, Jiali Yi, Xiaoju Hou, Yue Zhao, Wenxin Huang, Miao Zhang, Kui Xiao, Bing Yang

TL;DR
KeenKT introduces a probabilistic approach to knowledge tracing that models student knowledge states as distributions, capturing learning fluctuations and improving prediction accuracy over existing methods.
Contribution
The paper proposes KeenKT, a novel knowledge tracing model using distributional state representations and attention mechanisms to better handle student behavior variability.
Findings
Outperforms state-of-the-art models in accuracy and sensitivity.
Achieves up to 5.85% AUC and 6.89% ACC improvements.
Demonstrates robustness across six datasets.
Abstract
Knowledge Tracing (KT) aims to dynamically model a student's mastery of knowledge concepts based on their historical learning interactions. Most current methods rely on single-point estimates, which cannot distinguish true ability from outburst or carelessness, creating ambiguity in judging mastery. To address this issue, we propose a Knowledge Mastery-State Disambiguation for Knowledge Tracing model (KeenKT), which represents a student's knowledge state at each interaction using a Normal-Inverse-Gaussian (NIG) distribution, thereby capturing the fluctuations in student learning behaviors. Furthermore, we design an NIG-distance-based attention mechanism to model the dynamic evolution of the knowledge state. In addition, we introduce a diffusion-based denoising reconstruction loss and a distributional contrastive learning loss to enhance the model's robustness. Extensive experiments on…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Advanced Graph Neural Networks
